Electrostatic induction–based bionic perception method for low-altitude flying targets
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摘要: 针对现有传感技术难以满足低空飞行器对周围高速飞行目标实时精确感知的问题，受鲨鱼电场感受器官的启发，提出了一种基于静电感应原理的飞行目标仿生感知方法。建模分析了感应电极的姿态、飞行目标的距离和速度等参数对静电感应信号的影响规律；结合半球形电极阵列和高灵敏度静电传感器，实现了飞行目标静电信号差异化感知；利用电磁弹射装置和二维姿态控制系统，搭建了速度、轨迹等参数可调的带电飞行目标室内模拟试验环境，获得了全向来袭飞行目标的静电感应信号数据集；利用数据集训练符号回归模型，得到了反演飞行目标运动轨迹方向的数学模型，全向预测误差低于7.71°，验证了仿生感知方法的可行性。Abstract:
Background With the rapid development of the low-altitude economy, the number and speed of aircraft in partially open airspace are sharply increasing, intensifying the challenges of collision avoidance and safety management. Real-time perception of high-speed targets is therefore essential. However, existing sensors cannot meet the strict constraints of power, payload, and cost, highlighting the need for lightweight and energy-efficient alternatives.Purpose Aircraft accumulate electrostatic charges through triboelectric effects between blades or skins and atmospheric particles such as dust, ice crystals, or raindrops, as well as through jet charging from engine exhausts. These charges disturb the surrounding electrostatic field, which can be detected by electrostatic sensors. Previous studies have demonstrated trajectory and velocity detection using flat plates, cylindrical electrodes, vector field sensors, and even self-powered or deep learning–assisted approaches. Yet, such electrode arrays are typically bulky, aerodynamically intrusive, and unsuitable for low-altitude platforms. To address these gaps, this study introduces a bio-inspired electrostatic sensing approach.Methods Inspired by sharks’ Lorenzini ampullae, a conformal hemispherical electrode array was deployed to provide directional diversity and enhance spatial resolution. Theoretical modeling examined the effects of electrode orientation, target distance, and velocity on induction signals. A laboratory system was constructed using an electromagnetic launcher and attitude control module to fire charged metal spheres within a 4 m arena. A robotic platform equipped with the hemispherical array collected signals under two modes: central crossing and regional coverage. A symbolic regression model is then trained on the dataset to reconstruct the trajectory direction of flying targets.Results Models trained solely on central crossing data exhibited overfitting and poor generalization. In contrast, models trained on regional coverage data achieved consistent accuracy across datasets, with omnidirectional prediction errors below 7.71°and variations within 1°, demonstrating strong robustness and generalization.Conclusions This work presents a bio-inspired electrostatic sensing method for detecting charged flying targets. The system achieves accurate omnidirectional trajectory prediction with low error and minimal energy demand. The approach is passive, lightweight, low-cost, and easy to integrate, offering considerable potential for low-altitude airspace safety management. Future studies will focus on improving trajectory prediction accuracy and extracting multidimensional flight information.-
Key words:
- electrostatic induction /
- target perception /
- flying target /
- bionics /
- symbolic regression
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图 1 洛伦兹尼壶腹阵列启发的飞行目标静电感知示意图
Figure 1. Schematic of electrostatic perception for flying targets inspired by the Lorenzini tubule array
图 3 感应电极姿态参数对法向电场的影响规律
Figure 3. Influence of inductive electrode attitude parameters on the normal electric field
图 4 距离参数对归一化表面电荷密度的影响规律
Figure 4. Influence of distance parameters on the normalized surface charge density
图 6 测试飞行目标静电感应信号的测试场景与试验设计
Figure 6. Testing scenario and experimental design of flying target electrostatic induction signals
图 7 静电感应信号的数据清洗与特征提取
Figure 7. Data cleaning and feature extraction of electrostatic induction signals
图 8 模型在各数据集上的预测结果及误差
Figure 8. Prediction results and errors of model on different datasets
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